LDPC code is typically a linear stream of data in a self-correcting format that can be represented by an (m,n)-matrix with a relatively small, fixed number of ones (nonzero for arbitrary GF(q)) in each row and column, where m is the number of check bits and n is the code length in bits.
The most famous algorithm for decoding LDPC codes is called the iterative message-passing algorithm. Each iteration of this algorithm consists of two stages. In stage 1 (the row operations), the algorithm computes messages for all of the check nodes (the rows). In stage 2 (the column operations), the algorithm computes messages for all of the bit nodes (the columns), and sends them back to the check nodes associated with the given bit nodes. There are many different implementations of this message-passing algorithm, but all of them use two-stage operations. Further, in each of these implementations, the second step starts only after all of the messages for all of the rows have been calculated.
As with all information processing operations, it is desirable for the procedure to operate as quickly as possible, while consuming as few resources as possible. Unfortunately, LDPC codes such as those described above typically require a relatively significant overhead in terms of the time and the memory required for them to operate.
What is needed is an LDPC code that operates in a more efficient manner, such as by reducing the amount of time or the amount of memory that is required by the operation.